Characterizing skill acquisition through motor imagery with no prior physical practice.
Bibliographic record
Abstract
Motor learning depends upon plasticity in neural networks involved in the planning and execution of movement. Physical practice (PP) is the primary means of motor learning, but it can be augmented with nonphysical forms of practice including motor imagery (MI)-the mental rehearsal of movement. It is unknown if MI alone, without prior PP of a movement, can produce robust learning. Here the authors used an implicit sequence learning task to explore motor learning via MI alone or PP. Participants underwent implicit sequence learning training via MI (n = 31) or PP (n = 33). Posttraining reaction time was faster for implicit versus random sequences for both the MI group (M = 583 ± 84 ms; 632 ± 86 ms, d = 0.59) and PP group (M = 532 ± 73 ms; 589 ± 70 ms, d = 0.80), demonstrating that MI without PP facilitated skill acquisition. Relative to MI alone, PP led to reduced reaction time for both random (d = 0.65) and implicit sequences (d = 0.55) consistent with a nonspecific motor benefit favoring PP over MI. These results have broad implication for theories of MI and support the use of MI as a form of practice to acquire implicit motor skills. (PsycINFO Database Record
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".